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Record W2169607849 · doi:10.1109/iembs.2005.1617034

Measuring Minimum Critical Flow for Normal Breath Sounds

2005· article· en· W2169607849 on OpenAlexafffund
Azadeh Yadollahi, Zahra Moussavi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicPhonocardiography and Auscultation Techniques
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFlow (mathematics)Noise (video)SpectrogramAcousticsSound (geography)AudiologyRespiratory soundsMedicineSpeech recognitionMathematicsComputer sciencePhysicsInternal medicineMechanicsArtificial intelligence

Abstract

fetched live from OpenAlex

Relationship between respiratory sounds and flow has always been of interest for researchers and physicians. However, the flow-sound relationship at very low flow rate has been questionable because breath sounds must exceed a minimum flow in order to be audible and different from the background noise. This study aimed to find the minimum critical flow rates for respiratory sounds to be audible and different from background noise. Tracheal and lung sound signals of healthy subjects in two groups of adults (12 subjects) and children (9 subjects) were studied. The values of minimum critical flow were determined comparing the spectrogram of the respiratory sounds at very low flow with that of during breath hold. The values of minimum critical flow for tracheal sounds were found to be 3.7.. 1.7 ml/s/kg and 2.7 .. 1.7 ml/s/kg for adults and children, respectively. The minimum critical flows for lung sounds were found as 6.2 .. 1.8 ml/s/kg and 4.0 .. 2.9 ml/s/kg for adults and children, respectively.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.036
GPT teacher head0.306
Teacher spread0.269 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations8
Published2005
Admission routes2
Has abstractyes

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